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Modelling Group-Switching in Cluster Analysis via Compositional Mixture Components

delete2026-01-01
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PRE
AI
W
Welsh, Liam
J
Jeffrey L. Andrews *
B
Browne, Ryan
DOI：10.1007/s00357-026-09549-2delete
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Abstract

Abstract

En 中文
A non-Markovian framework for detecting and modelling observations that change groups across variables is introduced. This framework takes advantage of the concept of missing data, allowing straightforward implementation using the expectation-maximization algorithm for Gaussian mixture models. Importantly, the components used to model group-switching behaviour require only one additional free parameter to be estimated: making it a feasible approach for detecting and modelling rare group-switching events. We motivate the concepts underlying the techniques through simple examples, provide details for the model-fitting algorithms, and discuss results on real data.
Keywords:
Mixture models
EM algorithm
Missing data
Cluster analysis
Dynamic clustering
Group-switching

Journal

J
Journal of Classification
IF:
1.9
Papers:
31
Citations:
2.2K

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U
university of waterloo
Scholars:
2.4K
Papers: 1.3K
Citations: 1
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university of toronto
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Papers: 12.0W
Citations: 165
U
University of British Columbia
Scholars:
7.0W
Papers: 6.1W
Citations: 8.6W
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